From Treasury Masterminds
Everyone talks about “AI in treasury” as if it’s a switch you can flip between coffee breaks. It’s not. It’s a journey that starts with structure, not software. Below is a simple, pragmatic roadmap to move from ideas to impact—without wasting six months in PowerPoint purgatory.
1. Define the Problem, Not the Tool
Treasuries love tools. But AI is not a tool—it’s a means to solve a problem. Start by defining where you lose time, accuracy, or control today. Common use cases:
- Forecasting accuracy is inconsistent across subsidiaries.
- Manual reconciliations eat hours every week.
- Fraud or anomaly detection only happens after the fact.
Each of these can be linked to measurable outcomes (reduce forecast error by 20%, cut manual hours by 30%, etc.). That’s how you define “success” before the first algorithm is even considered.
2. Assess Your Data Reality
AI feeds on clean, structured, and accessible data. Treasury data rarely fits that description.
Ask yourself:
- Do we have a central data source (TMS, ERP, BI tool) or ten Excel sheets that only one person understands?
- Are files standardized, or is every entity on its own format?
- How often is data refreshed, validated, and reconciled?
Run a “data audit.” It’s the dullest but most crucial step. You can’t forecast tomorrow’s cash flow with confidence if half your data lives in inboxes.
3. Pick One Use Case and Prototype Fast
Don’t aim to “implement AI.” Aim to pilot one AI-powered use case. Start where:
- Data is available and clean enough.
- Value is tangible.
- Results can be measured.
Good first projects:
- Cash Flow Forecasting using machine learning on historical inflows/outflows.
- Payment anomaly detection to flag fraud or duplicate payments.
- Transaction categorization using NLP (natural language processing) to classify payments or counterparties automatically.
Run it as a 6–8 week pilot with clear KPIs. Prove the concept, then expand.
4. Build a Cross-Functional Team
AI projects fail when IT “owns it” or Treasury “outsources it.” They work when Finance, Treasury, Data Science, and IT collaborate.
Define roles early:
- Treasury: defines business logic and success metrics.
- Data/IT: prepares data and maintains infrastructure.
- Vendor/Consultant: supports model building.
- CFO sponsor: provides air cover when things stall.
This is also where you establish AI ownership inside finance—so you don’t rely forever on external vendors.
5. Implement Governance and Controls
AI doesn’t eliminate controls; it creates new ones.
Treasury should define:
- Model validation process (does the model still make sense over time?).
- Exception handling (what happens when AI is “unsure”?).
- Audit trail (you’ll still need to explain results to auditors).
Document decisions as you go. AI in treasury should improve transparency, not mystify it.
6. Educate and Upskill the Team
The fastest way to kill innovation is to let people feel excluded from it.
- Run internal sessions explaining how the model works (no code, just logic).
- Encourage curiosity instead of fear—AI won’t replace treasurers, but treasurers who use AI will outperform those who don’t.
- Build AI literacy into treasury training and performance metrics.
7. Measure, Iterate, and Scale
AI is not “done.” It learns—or dies.
After the pilot:
- Track KPIs monthly.
- Refine models when accuracy drops.
- Add new datasets (e.g., sales forecasts, macroeconomic indicators).
- Only scale to new use cases after proving ROI on the first one.
By this point, you should have an internal framework and the confidence to apply AI thinking to other areas—FX exposure management, investment optimization, or working capital analytics.
8. Communicate Results
Most CFOs don’t care about models—they care about outcomes. Quantify:
- Hours saved.
- Forecast error reduced.
- Fraud attempts caught.
- Cost savings achieved.
Share results internally. Celebrate small wins. It builds momentum for the next project and helps shift the narrative from “AI experiment” to “strategic advantage.”
Final Thoughts
An AI roadmap is not about chasing buzzwords—it’s about building digital muscle in treasury. The goal isn’t to have a “smart treasury.” The goal is to have a faster, more informed, less reactive one.
Start small, stay practical, and keep humans in charge of the machines. That’s how AI stops being a headline and starts being a habit.
Also Read
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